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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1495 条记录，以下是1001-1010 订阅

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Animation Geometry Compression Using the Linear Discriminant Analysis

Animation Geometry Compression Using the Linear Discriminant...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： A.R. Vamsidhar P.K. Bora Sanjib Das Department of Electronics and Communication Engineering Indian Institute of Technology Guwahati Guwahati India

We propose a new method to compress the geometry component of 3D animation sequence. It is based on the linear discriminant analysis (LDA) of the animation geometry data. The redundancy across the animation frames has been exploited by using the LDA in the temporal direction. Owing to the redundancy between the frames of a class, the covariance matrix of that class for the LDA computation may become singular. To overcome this drawback, we first transform the data into a new basis using the principal component analysis (PCA) and then apply the LDA on a few principal components. The reconstruction is simple and involves two stages: firstly for the LDA and then for the PCA. The experimental results show that the proposed method has the advantage of better reconstruction error at high compression ratios.

关键词： Animation Geometry Linear discriminant analysis Principal component analysis image coding image reconstruction Surface texture Rough surfaces Surface roughness Redundancy

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Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features 10

Classification of cell cycle phases in 3D confocal microscop...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Jaeger, Stefan Palaniappan, Kannappan Casas-Delucchi, Corella S. Cardoso, M. Cristina Department of Computer Science University of Missouri Columbia MO 65211 United States Department of Biology Technische Universität Darmstadt 64287 Darmstadt Germany

ISBN: (纸本)9781450300605

Cell cycle progression studies using subcellular markers offer important insight into cellular mechanisms of disease and therapeutic drug development. Due to the large volumes of microscopy data involved in such studies, a manual approach to extracting quantitative information is not only prohibitive but error prone. We present an automatic cell cycle phase identification algorithm applied to 3D spinning disk confocal microscopy imagery of mouse embryonic fibroblast cells. In our training dataset, each 3D image stack depicts a single cell in a manually identified cell phase, and is recorded via two channels showing the fluorescently marked protein PCNA and the chromocenters, respectively. We use a 3D k-means approach to segment each volume and extract a set of shape and curvature features to characterize the subcel-lular foci patterns associated with cell cycle phases for each channel. Radial features are used to describe the spatial distribution of PCNA over the course of the cell cycle. A support vector machine (SVM) classifier using 234 features was trained and achieved a recognition rate of 83% for the chromocenter and 86% for the PCNA channels separately on the testing data. A combined SVM classifier using both channels and 468 features further improved the accuracy to nearly 92% for five phases (G1, SE, SM, SL, G2) and shows promising scalability. © 2010 ACM.

关键词： Confocal microscopy

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Anomaly from motion: Unsupervised extraction of visual irregularity via motion prediction 6th

Anomaly from motion: Unsupervised extraction of visual irreg...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Majumder, Avishek Babu, R. Venkatesh Chakraborty, Anirban Indian Institute of Science BangaloreKarnataka560012 India

ISBN: (纸本)9789811300196

The problem of automatically extracting anomalous events from any given video is a problem that has been researched from the early days of computer vision. It has still not been fully solved, showing that it is indeed not a trivial problem. The various challenges involved are lack of proper definition, varying scene structure and objects of interest in the scene, just a few to name. In this paper we propose a novel method to extract outliers from motion alone. We employ a stacked LSTM encoder-decoder structure to model the regular motion patterns of the given video sequence. The discrepancy between the motion predicted using the model and the actual observed motion in the scene is analyzed to detect anomalous activities. We perform extensive experimentation on the benchmark datasets of crowd anomaly analysis. We report State of the Art results across all the datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network 7th

PoshakNet: Framework for Matching Dresses from Real-Life Pho...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Khaund, Abhigyan Thapar, Daksh Nigam, Aditya Indian Institute of Technology Mandi India

ISBN: (纸本)9789811586965

Online garment shopping has gained many customers in recent years. Describing a dress using keywords does not always yield the proper results, which in turn leads to dissatisfaction of customers. A visual search based system will be enormously beneficent to the industry. Hence, we propose a framework that can retrieve similar clothes that can be found in an image. The first task is to extract the garment from the input image (street photo). There are various challenges for that, including pose, illumination, and background clutter. We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing. It has been shown that GAN can retrieve the garment very efficiently despite the challenges of street photos. Finally, a siamese based matching system takes the retrieved cloth image and matches it with the clothes in the dataset, giving us the top k matches. We take a pre-trained inception-ResNet v1 module as a siamese network (trained using triplet loss for face detection) and fine-tune it on the shopping dataset using center loss. The dataset has been collected inhouse. For training the GAN, we use the LookBook dataset [14], which is publically available. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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REF-SHARP: REFined face and geometry reconstruction of people in loose clothingg± 13

REF-SHARP: REFined face and geometry reconstruction of peopl...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Routhu, Snehith Goud Sagar, Sai Sharma, Avinash IIIT Hyderabad India

ISBN: (纸本)9781450398237

In this paper, we address the problem of monocular 3D human reconstruction with an acute focus on the challenge of recovering person-specific facial geometry as well as suppressing surface noise, specifically addressing the issue of false geometrical variations caused by textural edges. Most of the existing state-of-the-art methods in this domain fail to address these challenges. More specifically, we propose to integrate facial and wrinkle map priors in a learning-based framework to improve the quality of full-body 3D reconstruction from monocular images. By incorporating facial prior, we recover person-specific identity unlike many of the existing methods which rely on parametric shape models. Similarly, the wrinkle map prior enables our network to alleviate the challenge of false geometrical variations caused by high-frequency textural details present in the input image. We evaluate our method on publicly available datasets & in-the-wild internet images with loose clothing and report superior performance both qualitatively and quantitatively when compared with SOTA methods. © 2022 ACM.

关键词： image reconstruction

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Single View Homography Estimation for an Inclined Textured Planar Surface: Overcoming the Inverse and Ill-Posed Challenge! 23

Single View Homography Estimation for an Inclined Textured P...

引用

proceedings of the fourteenth indian conference on computer vision, graphics and image processing

作者： Pooja Kumari Sukhendu Das IIT Madras IN Indian Institute of Technology Madras IN

ISBN: (纸本)9798400716256

Homography estimation is a crucial step in many computer vision problems involving the planar transformation of an image from one view to another. Generally, from two image views of a scene with a planar patch, one can compute the homography matrix (H) using Direct Linear Transformation (DLT) or its variants. In this study, we work on the inverse scenario to solve an ill-posed problem, where given only a single image (Math 1) of an inclined textured planar surface, we attempt to estimate both H and the orthogonal view (Math 2) of the planar surface. We propose to solve this problem of homography Estimation using an optimization framework. To the best of our knowledge, there has barely been any work done on this inverse scenario of homography estimation from only a single image view. The research presented in this paper successfully achieves image rectification and homography computation by assuming a perspective transformation on an upright planar patch when viewed by a camera placed exactly in front of it. A cost function with suitable constraints is utilized to refine homography estimation. Experimental results reveal the efficiency of the proposed approach in the case of images of checkerboard and other textured planar surfaces (real-world images with lines and other prominent textures).

关键词： DLT Homography LM image perspective optimization texture

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Data Analytics and Learning: proceedings of DAL 2018

引用

2018年

作者： P Nagabhushan Devanur S Guru B H Shekar Y H Sharath Kumar

ISBN: (纸本)9789811325137

This book presents new theories and working models in the area of data analytics and learning. The papers included in this volume were presented at the first International conference on Data Analytics and Learning (DAL 2018), which was hosted by the Department of Studies in computer Science, University of Mysore, India on 3031 March 2018. The areas covered include pattern recognition, image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems. As such, the book offers a valuable resource for researchers and practitioners alike.

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Colour Retinal image Enhancement Based on Domain Knowledge

Colour Retinal Image Enhancement Based on Domain Knowledge

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Gopal Datt Joshi Jayanthi Sivaswamy Centre for Visual Information Technology International Institute of Information Technology Hyderabad India

Retinal images are widely used to manually or automatically detect and diagnose many diseases. Due to the complex imaging setup, there is a large luminosity and contrast variability within and across images. Here, we use the knowledge of the imaging geometry and propose an enhancement method for colour retinal images, with a focus on contrast improvement with no introduction of artifacts. The method uses non-uniform sampling to estimate the degradation and derive a correction factor from a single plane. We also propose a scheme for applying the derived correction factor to enhance all the colour planes of a given image. The proposed enhancement method has been tested on a publicly available dataset. Results show marked improvement over existing methods.

关键词： Retina image enhancement Lighting image segmentation Color Diseases Cameras Surface topography Automatic control Histograms

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Feature prominence-based weighting scheme for video tracking 12

Feature prominence-based weighting scheme for video tracking

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Candemir, Sema Palaniappan, Kannappan Bunyak, Filiz Rao, Raghuveer M. Seetharaman, Guna Dept. of Computer Science University of Missouri Columbia MO United States Army Research Lab. Adelphi MD United States Air Force Research Lab. Rome NY United States

ISBN: (纸本)9781450316606

This paper introduces a new mechanism called Feature Prominence to combine evidence from multiple feature operators for more reliable target detection and localization during video tracking. Feature prominence is measured using the statistical p-value estimated from a non-parametric local kernel density estimate of the a posteriori feature distribution. More prominent features have lower p-values and this ordering can be used to either discard low prominence features (high p-values) or reduce their weight during the feature fusion process to produce a more reliable fused feature likelihood map for locating the target at a subsequent time during tracking. The proposed feature fusion method is embedded within a test-bed tracking system. Detection and tracking performance of feature prominence as well as three other fusion methods are evaluated using the peak-recall and distance accuracy measures. Experimental results show that feature prominence outperforms these other feature fusion methods. © 2012 ACM.

关键词： Target tracking

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G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniquesg± 13

G-PReDICT: Generalizable Person Re-ID using Domain Invariant...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Jambigi, Chaitra Masud, Umar Chakraborty, Anirban Dept. of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru India Dept. of Electronics and Communications Jamia Millia Islamia New Delhi India

ISBN: (纸本)9781450398237

Learning identity-Aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method. © 2022 ACM.

关键词： computer vision

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